A Method of Polarimetric Synthetic Aperture Radar Image Classification Based on Sparse Representation

Abstract

Sparse representation-based techniques have shown great potential for pattern recognition problems. Therefore, on the basis of the sparse characteristics of the features for PolSAR image classification, a supervised PolSAR image classification method based on sparse representation is proposed in this paper. It works by projecting the feature vector of the pending pixel onto a subset of training vectors from dictionary and then obtains the corresponding optimal coefficients as well as the residual error with respect to each atom. Then, the residual errors of the pending pixel with respect to each atom are evaluated and considered as the criteria for classification, namely, the ultimate class can be obtained according to the atoms with the least residual error. The verified experiment is implemented using Danish EMISAR L-band fully PolSAR data of Foulum Area(DK) to validate the performance of the proposed classification method.The preliminary experimental results confirm that the proposed method outputs an excellent result and moreover the classification process is simpler and less time consuming.